Report on the Second International Workshop on Data Management on Modern Hardware (DaMoN'06)

This report summarizes the presentations and discussions that occurred during the Second International Workshop on Data Management on Modern Hardware (DaMoN). DaMoN was held in Chicago on June 25th, 2006, and was collocated with ACM SIGMOD 2006. The aim of this one-day workshop is to bring together researchers interested in optimizing database performance on modern computing infrastructure by designing new data management techniques and tools

Topology-aware GPU scheduling for learning workloads in cloud environments

Recent advances in hardware, such as systems with multiple GPUs and their availability in the cloud, are enabling deep learning in various domains including health care, autonomous vehicles, and Internet of Things. Multi-GPU systems exhibit complex connectivity among GPUs and between GPUs and CPUs. Workload schedulers must consider hardware topology and workload communication requirements in order to allocate CPU and GPU resources for optimal execution time and improved utilization in shared cloud environments. This paper presents a new topology-aware workload placement strategy to schedule deep learning jobs on multi-GPU systems. The placement strategy is evaluated with a prototype on a Power8 machine with Tesla P100 cards, showing speedups of up to ≈1.30x compared to state-of-the-art strategies; the proposed algorithm achieves this result by allocating GPUs that satisfy workload requirements while preventing interference. Additionally, a large-scale simulation shows that the proposed strategy provides higher resource utilization and performance in cloud systems.This project is supported by the IBM/BSC Technology Center for Supercomputing collaboration agreement. It has also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493). We thank our IBM Research colleagues Alaa Youssef and Asser Tantawi for the valuable discussions. We also thank SC17 committee member Blair Bethwaite of Monash University for his constructive feedback on the earlier drafts of this paper.Peer ReviewedPostprint (published version

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Practical privacy enhancing technologies for mobile systems

Mobile computers and handheld devices can be used today to connect to services available on the Internet. One of the predominant technologies in this respect for wireless Internet connection is the IEEE 802.11 family of WLAN standards. In many countries, WLAN access can be considered ubiquitous; there is a hotspot available almost anywhere. Unfortunately, the convenience provided by wireless Internet access has many privacy tradeoffs that are not obvious to mobile computer users. In this thesis, we investigate the lack of privacy of mobile computer users, and propose practical enhancements to increase the privacy of these users. We show how explicit information related to the users' identity leaks on all layers of the protocol stack. Even before an IP address is configured, the mobile computer may have already leaked their affiliation and other details to the local network as the WLAN interface openly broadcasts the networks that the user has visited. Free services that require authentication or provide personalization, such as online social networks, instant messengers, or web stores, all leak the user's identity. All this information, and much more, is available to a local passive observer using a mobile computer. In addition to a systematic analysis of privacy leaks, we have proposed four complementary privacy protection mechanisms. The main design guidelines for the mechanisms have been deployability and the introduction of minimal changes to user experience. More specifically, we mitigate privacy problems introduced by the standard WLAN access point discovery by designing a privacy-preserving access-point discovery protocol, show how a mobility management protocol can be used to protect privacy, and how leaks on all layers of the stack can be reduced by network location awareness and protocol stack virtualization. These practical technologies can be used in designing a privacy-preserving mobile system or can be retrofitted to current systems

Postgraduate Statistics Centre:HEFCE Final Self Evaluation Report

The PSC is a subject-specific CETL that focuses entirely on postgraduate statistics education, encompassing all postgraduate teaching and learning activities within the Department of Maths and Statistics. Within the wider community the multi-disciplinary and multi-faceted issues facing PG education are wide ranging and challenging. The CETL initiative, and our membership of it, has enabled us broaden our horizons and gain a wider perspective, experience and appreciate a sense of community, and reflect upon and question current practice

Letter from the Special Issue Editor

Editorial work for DEBULL on a special issue on data management on Storage Class Memory (SCM) technologies